dask.array.nancumprod(x, axis, dtype=None, out=None, *, method='sequential')[source]

Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones.

This docstring was copied from numpy.nancumprod.

Some inconsistencies with the Dask version may exist.

Dask added an additional keyword-only argument method.

method{‘sequential’, ‘blelloch’}, optional

Choose which method to use to perform the cumprod. Default is ‘sequential’.

  • ‘sequential’ performs the cumprod of each prior block before the current block.

  • ‘blelloch’ is a work-efficient parallel cumprod. It exposes parallelism by first

    taking the product of each block and combines the products via a binary tree. This method may be faster or more memory efficient depending on workload, scheduler, and hardware. More benchmarking is necessary.

Ones are returned for slices that are all-NaN or empty.

New in version 1.12.0.

aarray_like (Not supported in Dask)

Input array.

axisint, optional

Axis along which the cumulative product is computed. By default the input is flattened.

dtypedtype, optional

Type of the returned array, as well as of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.

outndarray, optional

Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary.


A new array holding the result is returned unless out is specified, in which case it is returned.

See also


Cumulative product across array propagating NaNs.


Show which elements are NaN.


>>> np.nancumprod(1)  
>>> np.nancumprod([1])  
>>> np.nancumprod([1, np.nan])  
array([1.,  1.])
>>> a = np.array([[1, 2], [3, np.nan]])  
>>> np.nancumprod(a)  
array([1.,  2.,  6.,  6.])
>>> np.nancumprod(a, axis=0)  
array([[1.,  2.],
       [3.,  2.]])
>>> np.nancumprod(a, axis=1)  
array([[1.,  2.],
       [3.,  3.]])